For lane detection model, we used the excellent off-the-shelf lane detection algorithm named LaneNet [1]}, and its implementation on GitHub can be found on [2]}. We first retrain LaneNet by using pretrained model from TuSimple lane detection [3]} provided on [2]}. This lane detection model has 99 million FLOPs and 35 million number of parameters. Our lane detection data consist of 3500 images with 80:20 training and testing.
